In this tutorial we will discuss performing co-registration of the multiple imaging modalities to the T1-weighted image. ## Loading Data

library(ms.lesion)
library(neurobase)

Loading required package: oro.nifti

oro.nifti 0.7.3

files = get_image_filenames_list_by_subject()$training01
t1_fname = files["MPRAGE"]
t1 = readnii(t1_fname)

• Rigid-body registration (linear) - 6 degrees of freedom (dof)
  • Co-registration (within the same person)
    • Cross-sectional between-sequences
    • Longitudinal within-sequence
    • Longitudinal between-sequences
• Affine registration – 12 dof
• Non-linear (> 12 dof)
  • Usually require a prior affine registration
  • Across-subject registration
  • Registration to a template
    • There are many different templates

• Rigid-body registration (linear) - 6 degrees of freedom (dof)
  • Co-registration (within the same person)
    • Cross-sectional between-sequences
    • Longitudinal within-sequence
    • Longitudinal between-sequences
• Affine registration – 12 dof
• Non-linear (> 12 dof)
  • Usually require a prior affine registration
  • Across-subject registration
  • Registration to a template
    • There are many different templates

For a voxel \(v\), the rigid transformation can be written as:

\[T_{\rm rigid}(v) = Rv + t\] where \(R =\) \[\left[\begin{array}{ccc} \cos\beta\cos\gamma& \cos\alpha\sin\gamma + \sin\alpha\sin\beta\cos\gamma & \sin\alpha\sin\gamma - \cos\alpha\sin\beta\cos\gamma \\ -\cos\beta\sin\gamma & \cos\alpha\cos\gamma - \sin\alpha\sin\beta\sin\gamma & \sin\alpha\cos\gamma + \cos\alpha\sin\beta\sin\gamma \\ \sin\beta & -\sin\alpha\cos\beta & \cos\alpha\cos\beta \end{array}\right]\]

• 6 degrees of freedom
• \(3\) associated with the translation vector: \(t=(t_x, t_y, t_z)\)
• \(3\) associated with the rotation parameters: \(\theta=(\alpha, \beta,\gamma)\).

• Pitch - Think of nodding ("yes")
• Yaw - Think of shaking head ("no") (SMH)
• Roll - Think of shoulder shrugging ("I don't know")
• x – left/right
• y – forward/backward
• z – jump up/down

The registration function from extrantsr can register 2 images. The main arguments are:

• filename - either nifti object or filename of image to be registered (moving)
• template.file - either nifti object or filename of target image
• typeofTransform - transformation of moving to fixed image (Rigid/Affine)
• interpolator - how are voxels averaged in fixed space

It can also perform bias correction if correct = TRUE.

For example, if we wanted to register the FLAIR to the T1 image, we would run:

library(extrantsr)
reg = registration(filename = files["FLAIR"], template.file = files["MPRAGE"],
                   typeofTransform = "Rigid", interpolator = "linear")

The output in reg would contain the transformed image and paths to the estimated transformations.

First, we will register scans within a visit to the T1 of that visit:

We would like to perform registration within a visit. The extrantsr function preprocess_mri_within will do the following steps:

1. Inhomogeneity correction (N3 or N4)
2. Registration of the files to the first filename (T1)
3. Skull_stripping (if desired, we will perform this after)

The function within_visit_registration takes in:

• fixed image - the image to be registered to
• moving images - images to register to the fixed
• typeofTransform - transformation of moving to fixed image (Rigid/Affine)
• interpolator - how are voxels averaged in fixed space

and outputs a list of transformations and output filenames

res = within_visit_registration(
  fixed = files["MPRAGE"],
  moving = files[c("T2", "FLAIR", "PD")],
  correct = TRUE, correction = "N4",
  typeofTransform = "Rigid", 
  interpolator = "Linear"
)
output_imgs = lapply(res, function(x) x$outfile)
names(output_imgs) = c("T2", "FLAIR", "PD")
out = c(MPRAGE = list(t1), output_imgs)

multi_overlay(out)

double_ortho(out$MPRAGE, out$T2 )

• Overall, there seems to be good overlap after registration
• Although we ran the registration on the raw data, it is usually beneficial to do inhomogeneity correction before registration.

Now that the images are in the same space as the T1, if we skull-strip the T1 image, we can apply this mask to those images to extract brain tissues.

mask = readnii("../output/training01_01_mprage_mask.nii.gz")
sub_mask = applyEmptyImageDimensions(mask, inds = dd$inds)
masked_imgs = lapply(xout, mask_img, sub_mask)

orthographic(masked_imgs[[2]])

• Registration within a subject can be done in R
  • registration wraps around the reading/writing of images and applying transformations
  • double_ortho and ortho2 can provide some basic visual checks to assess registration quality
  • preprocess_mri_within and preprocess_mri_across are general wrapper functions to process MRI data
• Once images are registered in the same space, operations can be applied to all the images, such as:
  • Masking with a brain mask
  • Transforming images to new spaces with one modality

• Co-registration requires fewer degrees of freedom (usually 6)
  • sequences from the same individual/brain are more alike than images from different subjects
• Example analyses that do not require a reference template
  • Identify location-specific longitudinal changes within an individual
  • Tissue class or structural segmentation
  • Analysis of indvidual-subject change in intensities

We have only done registration within a subject, but many times you want to perform a population-level analysis. This requries registration to a template:

• The registration can be done for this as well, just the template.file is now the template image and filename is the subject image.
  • other files (in the same space) can be transformed using the other.files and other.outfiles arguments. Or:
  • ants_apply_transforms can be used to apply this transformations to the other files